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Commit 89dd504c authored by Gregory Ashton's avatar Gregory Ashton
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Clean up the fitting_with_x_and_y_errors tutorial 

1. Fix the mangled xerr/yerr in the plotting routine that caused an
Error in matplotlib 3.6+
2. Switch to the bilby_mcmc sampler to avoid pip installation of nestle
3. Run over the example and check it still works
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1 merge request!1157Clean up the fitting_with_x_and_y_errors tutorial
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%% Cell type:markdown id: tags:
# Fitting a model to data with both x and y errors with `Bilby`
Usually when we fit a model to data with a Gaussian Likelihood we assume that we know x values exactly. This is almost never the case. Here we show how to fit a model with errors in both x and y.
Since we are using a very simple model we will use the `nestle` sampler.
This can be installed using
```console
$ conda install -c conda-forge nestle
```
or
```console
$ pip install nestle
```
%% Cell type:code id: tags:
```
import bilby
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
```
%% Cell type:markdown id: tags:
### Simulate data
First we create the data and plot it
%% Cell type:code id: tags:
```
# define our model, a line
def model(x, m, c, **kwargs):
y = m * x + c
return y
# make a function to create and plot our data
def make_data(points, m, c, xerr, yerr, seed):
np.random.seed(int(seed))
xtrue = np.linspace(0, 100, points)
ytrue = model(x=xtrue, m=m, c=c)
xerr = xerr * np.random.randn(points)
yerr = yerr * np.random.randn(points)
xobs = xtrue + xerr
yobs = ytrue + yerr
xerr_vals = xerr * np.random.randn(points)
yerr_vals = yerr * np.random.randn(points)
xobs = xtrue + xerr_vals
yobs = ytrue + yerr_vals
plt.errorbar(xobs, yobs, xerr=xerr, yerr=yerr, fmt="x")
plt.errorbar(xtrue, ytrue, yerr=yerr, color="black", alpha=0.5)
plt.xlim(0, 100)
plt.show()
plt.close()
data = {
"xtrue": xtrue,
"ytrue": ytrue,
"xobs": xobs,
"yobs": yobs,
"xerr": xerr,
"yerr": yerr,
}
return data
data = make_data(points=30, m=5, c=10, xerr=5, yerr=5, seed=123)
```
%% Cell type:markdown id: tags:
### Define our prior and sampler settings
Now lets set up the prior and bilby output directory/sampler settings
%% Cell type:code id: tags:
```
# setting up bilby priors
priors = dict(
m=bilby.core.prior.Uniform(0, 30, "m"), c=bilby.core.prior.Uniform(0, 30, "c")
)
sampler_kwargs = dict(priors=priors, sampler="nestle", nlive=1000, outdir="outdir", verbose=False)
sampler_kwargs = dict(priors=priors, sampler="bilby_mcmc", nsamples=1000, printdt=5, outdir="outdir", verbose=False, clean=True)
```
%% Cell type:markdown id: tags:
### Fit with exactly known x-values
Our first step is to recover the straight line using a simple Gaussian Likelihood that only takes into account the y errors. Under the assumption we know x exactly. In this case, we pass in xtrue for x
%% Cell type:code id: tags:
```
known_x = bilby.core.likelihood.GaussianLikelihood(
x=data["xtrue"], y=data["yobs"], func=model, sigma=data["yerr"]
)
result_known_x = bilby.run_sampler(
likelihood=known_x,
label="known_x",
**sampler_kwargs,
)
```
%% Cell type:code id: tags:
```
_ = result_known_x.plot_corner(truth=dict(m=5, c=10), titles=True, save=False)
plt.show()
plt.close()
```
%% Cell type:markdown id: tags:
### Fit with unmodeled uncertainty in the x-values
As expected this is easy to recover and the sampler does a good job. However this was made too easy - by passing in the 'true' values of x. Lets see what happens when we pass in the observed values of x
%% Cell type:code id: tags:
```
incorrect_x = bilby.core.likelihood.GaussianLikelihood(
x=data["xobs"], y=data["yobs"], func=model, sigma=data["yerr"]
)
result_incorrect_x = bilby.run_sampler(
likelihood=incorrect_x,
label="incorrect_x",
**sampler_kwargs,
)
```
%% Cell type:code id: tags:
```
_ = result_incorrect_x.plot_corner(truth=dict(m=5, c=10), titles=True, save=False)
plt.show()
plt.close()
```
%% Cell type:markdown id: tags:
### Fit with modeled uncertainty in x-values
This is not good as there is unmodelled uncertainty in our `x` values.
Getting around this requires marginalisation of the true x values or sampling over them.
See discussion in section 7 of https://arxiv.org/pdf/1008.4686.pdf.
For this, we will have to define a new likelihood class.
By subclassing the base `bilby.core.likelihood.Likelihood` class we can do this fairly simply.
%% Cell type:code id: tags:
```
class GaussianLikelihoodUncertainX(bilby.core.likelihood.Likelihood):
def __init__(self, xobs, yobs, xerr, yerr, function):
"""
Parameters
----------
xobs, yobs: array_like
The data to analyse
xerr, yerr: array_like
The standard deviation of the noise
function:
The python function to fit to the data
"""
super(GaussianLikelihoodUncertainX, self).__init__(dict())
self.xobs = xobs
self.yobs = yobs
self.yerr = yerr
self.xerr = xerr
self.function = function
def log_likelihood(self):
variance = (self.xerr * self.parameters["m"]) ** 2 + self.yerr**2
model_y = self.function(self.xobs, **self.parameters)
residual = self.yobs - model_y
ll = -0.5 * np.sum(residual**2 / variance + np.log(variance))
return -0.5 * np.sum(residual**2 / variance + np.log(variance))
```
%% Cell type:code id: tags:
```
gaussian_unknown_x = GaussianLikelihoodUncertainX(
xobs=data["xobs"],
yobs=data["yobs"],
xerr=data["xerr"],
yerr=data["yerr"],
function=model,
)
result_unknown_x = bilby.run_sampler(
likelihood=gaussian_unknown_x,
label="unknown_x",
**sampler_kwargs,
)
```
%% Cell type:code id: tags:
```
_ = result_unknown_x.plot_corner(truth=dict(m=5, c=10), titles=True, save=False)
plt.show()
plt.close()
```
%% Cell type:markdown id: tags:
Success! The inferred posterior is consistent with the true values.
......
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